Memory coherence activity prediction in commercial workloads

Recent research indicates that prediction-based coherence optimizations offer substantial performance improvements for scientific applications in distributed shared memory multiprocessors. Important commercial applications also show sensitivity to coherence latency, which will become more acute in the future as technology scales. Therefore it is important to investigate prediction of memory coherence activity in the context of commercial workloads.This paper studies a trace-based Downgrade Predictor (DGP) for predicting last stores to shared cache blocks, and a pattern-based Consumer Set Predictor (CSP) for predicting subsequent readers. We evaluate this class of predictors for the first time on commercial applications and demonstrate that our DGP correctly predicts 47%-76% of last stores. Memory sharing patterns in commercial workloads are inherently non-repetitive; hence CSP cannot attain high coverage. We perform an opportunity study of a DGP enhanced through competitive underlying predictors, and in commercial and scientific applications, demonstrate potential to increase coverage up to 14%.